Comprehensive Business Data Curation and Mapping

@EightVeer

Hi there, We can help consolidate your CSV files, clean and normalize the records, and then research each business against your criteria so the final sheet is usable for outreach and visit planning. Our focus would be on accurate enrichment, careful cross-checking, and a clear shortlist of businesses that match your requirements. We’ll organize the data into a single Google Sheet, verify business status, confirm address and phone details, add publicly available owner and business contact information where confidently matched, and include concise business descriptions and supporting notes for any fields that need review. We’ll also keep the output structured so it is easy to map and work from in the field. Confidentiality and accuracy will be treated as core priorities throughout. Best Regards, 8veer

@salajka

Hi, Curating 5,000 businesses across 12 CSV files and verifying strict criteria (like revenue, exact owner emails, and franchise status) is impossible to do manually without massive errors. I will not do this by hand. I am an API Integration Architect and will build a custom, automated data pipeline to process your list. My Approach: Automated Merging: I will write a Google Apps Script to instantly merge your 12 CSV files into a master Google Sheet, stripping out the useless columns. Programmatic Verification: Instead of manually Googling 5,000 businesses, I will use the Google Places API and data enrichment APIs (like Apollo) to automatically verify if the business is still open, flag large franchises, and extract the exact owner's email address (not just info@). Route Mapping: Once the list is pared down to your golden 500, I will use the Google Maps API to generate an optimized route planner directly in the sheet, so you can get in your car and drive efficiently. Review my secure Google Drive folder to see my custom backend dashboards: [Insert Your Google Drive Link] I am based in Kochi, sharing your exact timezone. Let's get on a call to review a sample CSV so I can map out the API pipeline. Thanks and regards, Salaj Augustine

@Hrolgar

Before a single record gets cleaned, someone has to decide whether "Company Name", "Business Name", and "Org" in three different files map to the same column. That decision shapes the whole output schema and has to happen before any dedup logic makes sense. My approach: a two-pass Python pipeline. First pass audits all 12 files, generates a candidate column mapping with confidence scores for fuzzy matches, and flags ambiguous cases for quick review. Second pass runs the merge, dedup, normalization, and schema enforcement against the agreed mapping. Fully repeatable: re-run when source files update and you get the same consistent output. Deliverable: merged dataset in the agreed schema, the Python scripts, and a column-mapping document showing how each source column was handled. Three days from file access. Price: 37,500 INR. This is an indicative estimate from the brief; I'll give you a firm quote once I've seen the actual files. One thing worth confirming before I start: is there a primary unique key across the records (registration number, domain, phone), or do you need fuzzy dedup on name and address?